Accurate state estimation is an integral part of maintaining safe operating conditions of electric power systems and serves as input for control functionalities, such as economic dispatch and optimal power flow problems. State estimation can be divided into two categories, one is static state estimation and the other is dynamic state estimation.
In static state estimation, the system state is inferred using only measurements from the current snapshot in time. Dynamic state estimation uses information from prior measurements in addition to the current measurements to make an improved estimate. The dynamic state estimation is a large-scale nonlinear state estimation problem for a practical electric power system. The solution accuracy and efficiency of dynamic state estimation method plays a crucial role for the success of its real-time applications.
For example, the method described in CN 101615794A discloses a dynamic state estimation method for an electrical power system based on an unscented Kalman filter (UKF). However, this method is not fully distributed and thus requires communication with a central processor and a risk of communication bottleneck. Accordingly, there is still a need for a system and a method for dynamic state estimation of an electric power system.